Bivariate correlation can be used to determine if two variables are linearly related to each other.
Remember that you will want to perform a scatter
plot before performing the correlation (to see if the
assumptions have been met.) The command for correlation is found at Analyze | Correlate | Bivariate
(this is shorthand for clicking on the Analyze menu item at the top of the window, and then clicking
on Correlate from the drop down menu, and Bivariate from the pop up menu.):

The Bivariate Correlations dialog box will appear:

Select one of the variables that you want to correlate by clicking on it in the left hand pane of the
Bivariate Correlations dialog box. Then click on the arrow button to move the variable into the Variables
pane. Click on the other variable that you want to correlate in the left hand pane and move it into the
Variables pane by clicking on the arrow button:

Specify whether the test of significance should be one-tailed or two-tailed. (We won't
get to this topic for quite a while. For now, select the one-tailed test by clicking on
the circle to the left of "one-tailed".) You can click on the Options button to have
some descriptive statistics calculated. The Options dialog box will appear:

From the Options dialog box, click on "Means and standard deviations" to get some
common descriptive statistics. Click on the Continue button in the Options dialog box.
Click on OK in the Bivariate Correlations dialog box. The SPSS Output Viewer will appear.

In the SPSS Output Viewer, you will see a table with the requested descriptive statistics and
correlations. This is what the Bivariate Correlations output looks like:

The Descriptive Statistics section gives the mean, standard deviation, and number of
observations (N) for each of the variables that you specified. For example, the mean of
the extravert variable is 2.52, the standard deviation of the rather stay at home variable is 0.900,
and there were 46 observations (N) for each of the two variables.

The Correlations section gives the values of the specified correlation tests, in this case,
Pearson's r. Each row of the table corresponds to one of the variables. Each column also
corresponds to one of the variables. In this example, the cell at the bottom row of the
right column represents the correlation of extravert with extravert. Kind of silly, isn't it!
This correlation must always be 1.0 (why?). Likewise the cell at the middle row of the middle
column represents the correlation of rather stay at home with rather stay at home. It too, must always be 1.0.
The cell at middle row and right column (or equivalently, the
bottom row at the middle column) is more interesting. This cell represents the correlation of extravert and
rather stay at home (or rather stay at home with extravert -- it doesn't matter. Why?) There are three numbers in
these cells. The top number is the correlation coefficient. The correlation coefficient
in this example is -0.310. The middle number is the significance of this correlation; in
this case, it is .018. (The significance basically tells us whether we would expect a
correlation that was this large purely due to chance factors and not due to an actual
relation. In this case, it is improbable that we would get an r this big if there was not
a relation between the variables.) The bottom number, 46 in this example, is the number of
observations that were used to calculate the correlation coefficient.